Experts
I am a computational biologist trained in evolutionary genetics, and I use probabilistic modeling to investigate functional divergence within complex biological systems. I am cross-appointed at Dalhousie University in the Departments of Biology and Mathematics & Statistics. I supervise an inter-disciplinary research program with graduate students in both Biology and Statistics. My research exploits the simultaneous pursuit of biological questions and modeling advancements. I have traditionally worked in the area modeling and inference of functional divergence at the gene and genome level. More recent work involves the development of new models and methods to study multi-level processes in the context of explicit changes in organism phenotype. We have developed new models of the joint analysis of gene and phenotype evolutionary dynamics along a phylogenetic tree. We have also developed new process models to explore multi-level selection theory (MLST) in the context of gene – species – community level selective pressures.
Dr. Kong is a professor at the University of Toronto, where he serves as the director of the Artificial Intelligence (AI) and Mathematical Modeling Lab. Additionally, he is the Director of the Africa-Canada Artificial Intelligence and Data Innovation Consortium and the Global South Artificial Intelligence for Pandemic and Epidemic Preparedness and Response Network. He is also the Regional Node Liaison to the steering committee of the Canadian Black Scientist Network. He obtained his Ph.D. in Mathematics with a certificate in AI from the University of Alberta, his MSc in Engineering Mathematics from the University of Hamburg, Germany, and the University of L’Aquila, Italy. His B.Sc. in Computer Science and Mathematics was acquired at the University of Buea, Cameroon, and his B.Ed. in Mathematics was earned at University of Yaounde I, Cameroon. He did a 2-years of postdoc at Princeton University. Dr. Kong is an expert in AI, data science, mathematical modeling, and mathematics education. His principal research program focuses on designing and deploying AI, data science, and mathematical methodologies and technologies to build equitable, resilient governance strategies and increase societal preparedness for future global pandemics and climate disasters.
Dr. Julie Hussin is an Associate Professor in the Department of Medicine at the Université de Montréal (UdeM) and holds a Junior 2 fellowship from Fonds de Recherche du Québec en Santé. She is a core member of the Data Institute (IVADO) and an associate academic member at Mila (Quebec AI Institute). She is an expert in bioinformatics and evolutionary genomics, with extensive experience working with multi-omics datasets from large population cohorts. Her current research focuses on applying data science and machine learning techniques to improve cardiovascular health and pandemic preparedness.
She also serves as the Chair of Graduate Studies in Bioinformatics at UdeM and her commitment to Inclusion, Diversity, Equity, and Access (IDEA) is evident through her active participation in IDEA committees for the CIFAR AI for Health Imaging Solution Network and CMDO Network.
We are a computational biology group at McGill University School of Medicine’s Meakins-Christie Laboratories. Our mission is to unravel the intricacies of cell dynamics in various diseases, including developmental disorders, pulmonary diseases, and cancers. Deciphering these dynamics is crucial for comprehending disease pathogenesis and discovering novel therapies.
Our research leverages cutting-edge single-cell technologies, which offer unprecedented insights into individual cell states. We harness these technologies to drive discoveries and medical innovations in developmental and cancer biology. However, the complexity and heterogeneity of these diseases pose significant challenges in analyzing single-cell data.
Our primary focus is on developing machine learning techniques, particularly probabilistic graphical models, to comprehensively analyze, model, and visualize single-cell and bulk omics data, preferably in longitudinal or spatial contexts. These computational models deepen our understanding of cell dynamics across diverse biological systems. Ultimately, our work aims to advance public health through machine-learning-driven diagnostic and therapeutic strategies.
Dr. Jüri Reimand is a principal investigator at the Ontario Institute for Cancer Research (OICR) and associate professor at the University of Toronto, Canada. His lab focuses on computational biology, cancer genomics, and development of statistical and machine-learning methods. Areas of interest include interpretation of the non-coding genome and driver mutations, integrative analysis of multi-omics data through pathway and network information, and discovery of molecular biomarkers.
Kay is a Professor in the School of Computing Science at Simon Fraser University, Canada. His research interests are in RNA structure prediction, RNA visualization, CRISPR-Cas sgRNA design and other related applications. He enjoys developing innovative machine learning and optimization techniques for practical applications, but he is also interested in studying and developing new fundamental machine learning approaches. One such problem is developing neural networks that adapt their activation functions based on the underlying task. His lab developed the RnaPredict software for RNA folding, the jViz.RNA package for RNA visualization including pseudoknots, and the EvoDNN software for self-adaptive deep neural networks.
Keegan (she/her/hers) is an assistant professor in the Department of Statistics at the University of British Columbia and an investigator in the Centre for Molecular Medicine and Therapeutics at the BC Children’s Hospital Research Institute. She is also a faculty member in the Bioinformatics and Genome Science and Technology graduate programs at UBC. Her research group tackles the challenging task of uncovering meaningful biological insights from large-scale genomic experiments. Innovative technologies now allow scientists to probe the genome in more dimensions and at higher resolution than ever before, providing a wealth of information for studying the genomic basis of complex traits. However, discoveries from these new technologies can often be masked by technical artifacts, systematic biases, or low signal-to-noise ratio – think “needle in a haystack”. Keegan leads a team of researchers that focuses on developing novel frameworks and rigorous inferential procedures that exploit the increased scope and scale of high-throughput sequencing data, with the ultimate goal of uncovering new molecular signals in cancer, child health, and development.
Khanh Dao Duc got his PhD in applied mathematics in 2013 from the Ecole Normale Superieure (Paris, France) under the supervision of Dr. David Holcman, and did his postdoctoral training with Professor Yun Song at UC Berkeley and UPenn from 2014 to 2019. He joined UBC in 2019 as an assistant professor in Mathematics and associate member in Computer Science, where he runs an interdisciplinary group that develops theoretical and computational methods and tools for analyzing biological data and study various biological processes across biological scales. Recent works include computational methods for Cryo-EM, database and web application for ribosome structures, pipeline to analyze Ribo-seq data, cell shape analysis and algorithm from AFM and fluorescence image data, ML methods for interpreting electronic health records.
Kieran received his BSc from the University of Edinburgh in Mathematical Physics followed by a masters in Computational Biology at Cambridge University and a DPhil (PhD) in statistical genomics at Oxford University. He was subsequently a Banting postdoctoral fellow at the University of British Columbia and BC Cancer Agency (2017-2019). He is now Principal Investigator & Scientist at the Lunenfeld-Tanenbaum Research Institute, an Assistant Professor in the Departments of Molecular Genetics and Statistical Sciences, University of Toronto, and affiliate faculty at the Ontario Institute for Cancer Research.
Lingling Jin received her Ph.D. degree in Computer Science specializing in Bioinformatics from the University of Saskatchewan. She is currently an Assistant Professor of Computer Science at the University of Saskatchewan and an adjunct faculty member at Thompson Rivers University. Her primary research interest is in the computational modelling of genome evolution and various aspects of Comparative Genomics and Phenomics with specific attention on flowering plants. Her research aims to improve our understanding of plant genomes and the consequences of genome evolution.
Dr. Strug is a Professor in the Departments of Statistical Sciences, Computer Science and cross-appointed in Biostatistics at the University of Toronto and is a Senior Scientist in the Program in Genetics and Genome Biology at the Hospital for Sick Children. She is the Lead of the Canadian Cystic Fibrosis (CF) Gene Modifier Study, Co-Lead of the International CF Gene Modifier Consortium, Director of the Ontario Regional Centre of the Canadian Statistical Sciences Institute (CANSSI) and the inaugural Academic Director of the Data Science Initiative (DSI) at the University of Toronto. As a statistical geneticist, her research focuses on the development of novel statistical approaches to analyze and integrate multi-omics data to identify genetic contributors to complex human disease. She has received several honours and awards including the Tier 1 Canada Research Chair in Genome Data Science.